At The End Of Happy Hour!¶

Project 1

Team members¶

  • Anthony Garcia
  • Katrina Fletcher
  • Nancy Campos
  • Scott Dutton

PREMISE¶

Our group wanted to find the correlation of alcohol consumption in comparison to happiness worldwide.  Based
on data gathered from 112 countries, we were able to determine the top five "happiest" countries and the 
bottom 5 least "happiest" countries.

The happiness score is based on a scale from 0 to 10 with 10 being the happiest.
In [2]:
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import os
import numpy as np
from scipy.stats import linregress
import gmaps
import plotly.express as px
In [3]:
happiness_path = os.path.join("Resources", "HappinessAlcoholConsumption.csv")
income_path = os.path.join("Resources", "countries_income_group.csv")
country_path = os.path.join("Resources", "world_country_and_usa_states_latitude_and_longitude_values.csv")
In [4]:
happiness_study_df = pd.read_csv(happiness_path)
income_study_df = pd.read_csv(income_path, index_col=[0])
df_lat = pd.read_csv(country_path)
In [5]:
df_lat = df_lat[['latitude','longitude','country']]
df_lat.columns = ['latitude','longitude','Country']
df_lat = pd.merge(happiness_study_df,df_lat,on='Country')
In [6]:
income_study_df.rename(columns = {'Economy' : 'Country'}, inplace = True)
In [7]:
alcohol_study = pd.merge(happiness_study_df, income_study_df, on = "Country")
In [110]:
final_alcohol = alcohol_study.drop(columns=['Code', 'Region_y'])
final_alcohol
Out[110]:
Country Region_x Hemisphere HappinessScore HDI GDP_PerCapita Beer_PerCapita Spirit_PerCapita Wine_PerCapita Income group
0 Denmark Western Europe north 7.526 928 53.579 224 81 278 High income
1 Switzerland Western Europe north 7.509 943 79.866 185 100 280 High income
2 Iceland Western Europe north 7.501 933 60.530 233 61 78 High income
3 Norway Western Europe north 7.498 951 70.890 169 71 129 High income
4 Finland Western Europe north 7.413 918 43.433 263 133 97 High income
... ... ... ... ... ... ... ... ... ... ...
107 Madagascar Sub-Saharan Africa south 3.695 517 402.000 26 15 4 Low income
108 Tanzania Sub-Saharan Africa south 3.666 533 878.000 36 6 1 Low income
109 Liberia Sub-Saharan Africa north 3.622 432 455.000 19 152 2 Low income
110 Benin Sub-Saharan Africa north 3.484 512 789.000 34 4 13 Low income
111 Togo Sub-Saharan Africa north 3.303 500 577.000 36 2 19 Low income

112 rows × 10 columns

Question to answer¶

What Are the top 5 Happiest Countries?¶

In [112]:
#Filter to find top5 happiest countries 
top5 = final_alcohol.loc[final_alcohol['HappinessScore'] > 7.41]
top5
Out[112]:
Country Region_x Hemisphere HappinessScore HDI GDP_PerCapita Beer_PerCapita Spirit_PerCapita Wine_PerCapita Income group
0 Denmark Western Europe north 7.526 928 53.579 224 81 278 High income
1 Switzerland Western Europe north 7.509 943 79.866 185 100 280 High income
2 Iceland Western Europe north 7.501 933 60.530 233 61 78 High income
3 Norway Western Europe north 7.498 951 70.890 169 71 129 High income
4 Finland Western Europe north 7.413 918 43.433 263 133 97 High income
In [ ]:
#variables for bar graph
x = top5['Country']
y = top5['HappinessScore']

color = ['#1053e3', '#1072e3', '#1092e3','#10abe3','#10bce3']

#plot the bar graph
plt.bar(x,y, color=color)

#add labels and styling to graph
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Countries', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Top Five Happiest Countries', fontsize=25, loc='center', fontweight='bold')
plt.ylim(7.35,7.55)
fig = plt.gcf()
fig.set_size_inches(20,10)

plt.show()
In [113]:
In [ ]:

Question to answer¶

What are the bottom 5 happiest countries?¶

In [115]:
#Filter to find bottom5 least happiest countries
bottom5 = final_alcohol.loc[final_alcohol['HappinessScore'] <= 3.7]
sortbottom= bottom5.sort_values('HappinessScore', ascending=True)
In [ ]:
#variables for graph
x = sortbottom['Country']
y = sortbottom['HappinessScore']
color = ['#E3504D', '#E3754D', '#E39B4D','#E3B94D','#E3D74D']
#plot the graph
plt.bar(x,y, color=color)
#labeling and stying for graph
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Countries', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Bottom Five Least Happiest Countries',fontsize=25, loc='center', fontweight='bold')
plt.ylim(3.0,3.8)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [116]:
In [ ]:

Question to answer¶

Which Alcohol is the most popular?¶

In [118]:
beer = final_alcohol['Beer_PerCapita'].mean()
spirit = final_alcohol['Spirit_PerCapita'].mean()
wine = final_alcohol['Wine_PerCapita'].mean()
In [119]:
best_alcohol = pd.DataFrame({"Average Beer Per Capita" : [beer],
                            "Average Spirit Per Capita" : spirit,
                            "Average Wine Per Capita" : wine})

best_alcohol.head()
Out[119]:
Average Beer Per Capita Average Spirit Per Capita Average Wine Per Capita
0 141.258929 100.071429 70.276786
In [ ]:
alcohol = ["Beer", "Spirit", "Wine"]
avg_pc = [141.26, 100.07, 70.28]
x_axis = np.arange(len(avg_pc))

plt.bar(x_axis, avg_pc, color = "g", alpha = .8, align= 'center')
plt.xticks(x_axis, alcohol,fontsize=15, fontweight='bold')
plt.title("Beer vs Wine vs Spirit per Capita",fontsize=25, loc='center', fontweight='bold')
plt.xlabel("Alcohol",fontsize=15, labelpad=35)
plt.ylabel("Average Alcohol Type Per Capita",fontsize=15, labelpad=35)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [120]:

Question to answer¶

For the top 5, what is their alcohol of choice?¶

In [ ]:
top5 = final_alcohol.loc[final_alcohol['HappinessScore']>7.41]

x_axis = np.arange(len(top5['Country']))

plt.bar(x_axis -.10, top5['Beer_PerCapita'], width=0.2, label = 'Beer')
plt.bar(x_axis +.10, top5['Spirit_PerCapita'], width=0.2, label = 'Spirits')
plt.bar(x_axis +.10*3, top5['Wine_PerCapita'], width=0.2, label = 'Wine')
plt.title("Alcohol of Choice for Top Five",fontsize=25, loc='center', fontweight='bold')
plt.xticks(x_axis, top5['Country'],fontsize=15, fontweight='bold')

plt.legend(fontsize=15)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [122]:

Question to answer¶

For bottom 5, what is their alcohol of choice?¶

In [ ]:
bottom5 = final_alcohol.loc[final_alcohol['HappinessScore']<=3.7]

x_axis = np.arange(len(bottom5['Country']))

plt.bar(x_axis -.10, bottom5['Beer_PerCapita'], width=0.2, label = 'Beer')
plt.bar(x_axis +.10, bottom5['Spirit_PerCapita'], width=0.2, label = 'Spirits')
plt.bar(x_axis +.10*3, bottom5['Wine_PerCapita'], width=0.2, label = 'Wine')
plt.title("Alcohol of Choice for Bottom Five",fontsize=25, loc='center', fontweight='bold')
plt.xticks(x_axis, bottom5['Country'],fontsize=15, fontweight='bold')

plt.legend(fontsize=15)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [124]:

Question to answer¶

Is there a relationship between income level and happiness? Are they highly correlated?¶

In [ ]:
x = final_alcohol['Income group']
y= final_alcohol['HappinessScore']

plt.scatter(x,y,s=500, edgecolor='k', facecolors='#32a852')

plt.ylim(3,8)
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Income Level', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Income vs. Happiness Score',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [126]:
In [ ]:
# Calculate the correlation coefficient and linear regression model 
# for GDP per Capita and HappinessScore

GDP_per = final_alcohol.iloc[:,5]
Happiness_score = final_alcohol.iloc[:,3]
correlation = st.pearsonr(GDP_per,Happiness_score)
print(f"The correlation between GDP per Capita and Happiness Score is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['HappinessScore']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(5.5,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Happiness Score", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Happiness Score',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [127]:
 

The correlation between GDP per Capita and Happiness Score is -0.49.

Question to answer¶

Is there a relationship between GDP and each alcohol of choice?¶

In [ ]:
GDP_per = final_alcohol.iloc[:,5]
Beer_score = final_alcohol.iloc[:,6]
correlation = st.pearsonr(GDP_per,Beer_score)
print(f"The correlation between GDP per Capita and Beer Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Beer_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Beer Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Beer Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [129]:
 

The correlation between GDP per Capita and Beer Per Capita is -0.4.
In [ ]:
GDP_per = final_alcohol.iloc[:,5]
Wine_score = final_alcohol.iloc[:,8]
correlation = st.pearsonr(GDP_per,Wine_score)
print(f"The correlation between GDP per Capita and Wine Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Wine_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Wine Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Wine Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [130]:
 

The correlation between GDP per Capita and Wine Per Capita is -0.21.
In [ ]:
GDP_per = final_alcohol.iloc[:,5]
Spirit_score = final_alcohol.iloc[:,7]
correlation = st.pearsonr(GDP_per,Spirit_score)
print(f"The correlation between GDP per Capita and Spirit Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Spirit_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Spirit Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Spirit Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()
In [131]:
 

The correlation between GDP per Capita and Spirit Per Capita is -0.28.

Heat Map of Country versus Happiness Scores¶

In [142]:
In [8]:
fig = px.choropleth(df_lat, 
                    locations="Country", 
                    color="HappinessScore", 
                    locationmode = 'country names', 
                    hover_name="Country",
                    range_color=[0,8],
                    title='Country vs happiness score')
fig.show()
In [ ]: